基于神经网络的埋地管道防腐层缺陷检测

本文介绍了一种对埋地管道施加小幅度恒电流阶跃信号检测防腐层状态的电化学测试方法.提出利用人工神经网络对检测的响应数据进行智能判断,建立了防腐层状态判断网络模型.现场实验证明,用这种方法可以对埋地管道防腐层缺陷进行检测.

A Neural- based Detection Method of Buried Pipeline Coating Default

There are two kinds of coating defaults, coating defect and coating disbondment. When the coating is disbonded, the shielding effect of the coating to the cathodic protection current will make the shielded area not adequately protected and then corrosion occurs. An electrochemical detection method to test defaults in the anticorrosive coating of buried pipelines, which is applied to small amplitude lasting step current signal, is introduced in this paper. The artificial neural network is used to intelligently analyze the detecting response data and therefore an artificial neural network model is established. The field test results are analyzed and the test results indicated that the method is feasible to detect coating default.